MCP servers

genpark-knowledge-graph-entity-relation-triplet-extractor-skill

Enables agents to build and query structured semantic graphs, extracting, traversing and multi-hop reasoning over entity-relation-object triples while producing Cypher and JSON-LD graph output. It also combines BM25 lexical and dense vector retrieval with rank fusion, semantic caching and context reordering for grounded agentic memory.

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Enables extraction of subject-predicate-object triples from text and multi-hop reasoning over connected data, producing Cypher and JSON-LD outputs for agent memory, vector search, and retrieval-augmented generation workflows.

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Enables MCP clients to manage LLM inference memory through virtual paged-attention KV-cache block mapping, non-contiguous physical page allocation, zero-copy fragmentation tracking, radix-trie prefix caching, INT8 quantized compute, and speculative decoding verification. It also exposes prefill and decode latency telemetry so edge deployments can be benchmarked and tuned without external dependencies.

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Enables MCP clients to resolve legal contract clause cross-references and build defined-term dependency graphs, flagging circular references and substituting statutory definitions. It also parses document layouts and complex financial/legal tables, returning structured matrices and audit telemetry with zero external dependencies.

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Enables agents to run zero-dependency statistical modeling and data analysis through MCP, including multivariate linear regression via gradient descent, anomaly detection, time-series forecasting, hypothesis testing, and PCA dimensionality reduction.

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Enables agile sprint backlog velocity calculation and milestone completion prediction through the Model Context Protocol, integrating with MCP-compliant clients for deterministic, low-latency forecasting.

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Enables long-horizon autonomous task checkpointing and resumption through cryptographic event-sourced state persistence, idempotency key deduplication, timeout heartbeats, human-in-the-loop pause/resume tokens, and fail-safe recovery.

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Enables real-time voice agents to dynamically tailor speech pitch, cadence, and pause inflections through low-latency prosody, SSML, and emotion pacing modulation.

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genpark-managed-agent-fleet-lifecycle-supervisor-skill

Enables enterprise agent fleets to register workers, dispatch tasks by capability, monitor continuous heartbeat telemetry, trip automated circuit breakers, and recover from checkpointed state through a zero-dependency MCP server.

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Enables orchestration of worker registration, capability-based task dispatch, heartbeat telemetry, automatic circuit breaking, and checkpoint recovery for distributed agent operations.

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genpark-markdown-table-to-json-transformer-skill

Enables agents to transform tabular Markdown content into structured, type-inferred records with normalized formatting through an MCP-compatible interface.

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Enables converting Markdown and HTML tables into structured JSON records with automatic column type inference and format normalization. It also offers supporting web-agent primitives such as DOM semantic pruning, form field auto-mapping, and execution trajectory evaluation, all via a zero-dependency MCP server.

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Enables agents to compute shortest paths, topological dependency order, maximum flow and minimum-cut bottlenecks, minimum spanning trees, and PageRank centrality over weighted graphs for routing and execution planning. It exposes these graph-analysis capabilities as MCP tools through a zero-dependency Python server.

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Enables AI agents to construct SHA-256 Merkle trees and generate/verify inclusion proofs, validate JWT HMAC-SHA256 claims, run Schnorr zero-knowledge identification, and enforce constant-time secret comparison with nonce replay protection for tamper-evident state attestation. It runs as a zero-dependency, stdlib-only MCP stdio server for clients such as Claude Desktop, Cursor, and Windsurf.

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Enables AI agents to combine ambient multimodal perception, continuous episodic memory, and recency-decay context briefing for fast deterministic decisions and safe tool routing. It integrates with MCP clients to reduce unnecessary frontier LLM calls while enforcing token budgets and local-first memory retention.

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Enables personal AI agents to combine Meta Ray-Ban audio/vision sensory grounding with long-term episodic life memory graphs and empathetic emotional resonance. Integrates with MCP-compliant clients via JSON-RPC stdio for ambient, context-aware interactions.

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Enables MCP clients to compute shortest paths, topological dependency orderings, maximum network flows, minimum spanning trees, and PageRank-style centrality over graph topologies using only the Python standard library. It supports optimal multi-agent communication backbone and execution-plan analysis without external graph dependencies.

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Enables coding agents to analyze monorepo dependency graphs, detect and resolve circular dependencies, and plan a topological build order via Tarjan SCC and Kahn algorithms. It exposes deterministic, zero-dependency Python AST static analysis over MCP for auditing and refactoring codebases.

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Provides native MCP tools for quantitative-finance and risk-modeling workflows, enabling computation of option Greeks, simulation of geometric Brownian motion asset-price paths, historical/parametric VaR and CVaR estimates, bond duration/convexity metrics, and Nelson-Siegel yield-curve interpolation using only Python's standard library.

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Enables AI agents to run structured multi-persona debates over high-stakes decisions and merge their positions into an explainable consensus, complete with logged dissenting minority opinions. It runs as a zero-dependency Python MCP server or importable module, compatible with Claude Desktop, Cursor, and custom agent frameworks.

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Enables deterministic multi-agent debate cross-examination and consensus arbitration through the Model Context Protocol, with structured JSON output and zero external dependencies.

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genpark-multi-agent-speculative-execution-coordinator-skill

Enables coordinating multiple speculative agent execution paths by tracking milestone validation scores and canceling inferior branches early to reduce inference overhead.

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Enables MCP clients to run bounded epidemic dissemination, causal ordering, conflict-free state merging, and quorum-based transaction coordination across autonomous agent swarms.

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genpark-multi-agent-token-bucket-gossip-protocol-skill

Enables autonomous agent swarms to coordinate and reach deterministic convergence through epidemic dissemination, causal vector clocks, CRDT reconciliation, quorum-based leader election, and atomic two-phase commit over MCP. It also supports bounded network traffic via rate limiting and conflict-free replication.

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